Article: Computer vision-driven automated generation and style simulation of calligraphic fonts Journal: International Journal of Information and Communication Technology (IJICT) 2025 Vol.26 No.32 pp.1 - 16 Abstract: As computer vision technology grows quickly, the automatic creation and stylistic replication of calligraphic fonts has become a major area of study in digital art and cultural heritage. This paper suggests a multi-task learning model that combines a convolutional neural network (CNN) and a generative adversarial network (GAN). The conditional generative adversarial network (cGAN) is used to do the style migration and to make high-quality calligraphic fonts automatically and to fine-tune the simulation of different styles. The method works better than several popular generation methods when it comes to image structure fidelity, style expressiveness, and perceptual consistency, showing that it has both artistic and practical significance. This paper's research gives new ideas and technical help for using computer vision to digitise traditional art. Inderscience Publishers - linking academia, business and industry through research

Title: Computer vision-driven automated generation and style simulation of calligraphic fonts

Authors: Kun Fu

Addresses: School of Fine Arts and Design, Changji University, Changji 831100, China

Abstract: As computer vision technology grows quickly, the automatic creation and stylistic replication of calligraphic fonts has become a major area of study in digital art and cultural heritage. This paper suggests a multi-task learning model that combines a convolutional neural network (CNN) and a generative adversarial network (GAN). The conditional generative adversarial network (cGAN) is used to do the style migration and to make high-quality calligraphic fonts automatically and to fine-tune the simulation of different styles. The method works better than several popular generation methods when it comes to image structure fidelity, style expressiveness, and perceptual consistency, showing that it has both artistic and practical significance. This paper's research gives new ideas and technical help for using computer vision to digitise traditional art.

Keywords: computer vision; calligraphic fonts; automated generation; style simulation.

DOI: 10.1504/IJICT.2025.148490

International Journal of Information and Communication Technology, 2025 Vol.26 No.32, pp.1 - 16

Received: 17 Jun 2025
Accepted: 08 Jul 2025
Published online: 08 Sep 2025 *

Title: Computer vision-driven automated generation and style simulation of calligraphic fonts

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